Welcome to the homepage of DRBoaST

Disaster Resiliency through Big Open Data and Smart Things
(DRBoaST) is a three-year applied research project funded by Academia Sinica
Sustainability Science Research Program. It is a continuation of Project
OpenISDM (Open Information Systems for Disaster Management), which ended in
2015. The main focus of OpenISDM was on a framework for building open and
sustainable disaster management information systems. In contrast, the main
focus of DRBoaST is on the generation and use of data. One of its objectives is
to develop methods and tools for capturing, collection and generation of
critically needed but missing data and information for disaster risk reduction.
The other objective is to develop applications of available big and open data
for disaster preparedness and responses, especially applications and services
that can exploit open data and Internet of things to help us minimize personal
dangers and reduce property damages and economic losses when disasters strike.

Subprojects SiDiRC, RTEIC, and CSAI are extensions of
OpenISDM efforts. The research directions of subprojects DiSRC and ADiPLE are
new. They were motivated by critical needs and research and advanced
development opportunities.

Subproject SIDiRC will develop
a virtual community-specific disaster information cloud consisting of databases
for high disaster risk communities in Taiwan. The fine-grain GIS data and
information in the database for each community are generated and kept up to
date with the help of residents of the community. They supplement the GIS data
in government DMIS to support community-specific disaster risk reduction
decisions and operations.

Subproject RTEIC aims to
build a virtual real-time earthquake information cloud for disaster
preparedness and response. On the one hand, the subproject will enhance TESIS
(Taiwan Earthquake Science Information System) [1] built within the OpenISDM
project with new capabilities made feasible by continuing advances in sensor
and analysis technologies. On the other hand, the subproject will develop the
underlying earth science and methods for using GIS information and
observational data on earthquake-induced geo-hazards crowdsourced from trained
volunteers immediately after significant earthquakes for many purposes.
Examples include fine-scale assessments of damages and new disaster risks and
predictions of earthquake-triggered compound disasters.

When crowdsourcing
observational data, a disaster surveillance system must be able to make
effective use of qualified volunteers, guide them in their exploration of the
threatened area and process reports from them in real-time to extract decision
support information of good and quantifiable quality. CROSS (CROwdsouring
Support system for disaster Surveillance) [2-5] was designed and partially
prototyped in OpenISDM project to meet these critical needs since modern
platforms for crowdsourcing and mapping crisis information (e.g., Ushahidi) and
participatory sensing lack the required capabilities. Subproject CSAI will
enhance existing components of CROSS and integrate them into Ushahidi.

Subproject DiSRC was motivated the fact that
existing disaster historical records are almost solely for human consumption.
Their effective usages are hampered and limited to a great extent: It is nearly
impossible to extract from the records machine-readable data as input to modern
analysis and simulation tools for purposes of assuring data completeness and
consistency, assessing risk reduction strategies, tuning standard operating
procedures, educating the public and so on. The subproject aims to develop
disaster scenario capture, quality assurance and real-time quality control, and
history record authoring technologies needed to produce machine-readable, high
quality 3D and 4D historical data and information that can be processed by
tools and can be easily translated into human readable form to provide input to
authors of human-readable historical records.

Today, disaster alerts and
early warnings sent by authorities in most part of developed world are in the
standard CAP (Common Alert Protocol[1])
format and machine readable. The alerts are processed by emergency alert
systems/services, which in turn warn people via mobile phones and multimedia. A
better alternative is to deliver CAP alerts directly to smart devices and
mobile APPs and active emergent response systems (AERS) [6, 7] containing them
and have them process the alerts and take actions in location- and
environment-specific ways to keep us from harm and minimize property damages
and economical loss when the disasters forewarned by the alerts strike.
Subproject ADiPLE will develop the elements of technology and infrastructure
needed to enable the pervasive use of AERS within smart homes, buildings and
environments and exploit the use of 3D-4D data and information provided by BIM
(Building Information Model) and facility management systems to support
decisions of AERS of large, complex buildings.

Anticipated accomplishments and deliverables
include a virtual community-specific disaster information cloud; a virtual
real-time earthquake information cloud; platform, APPs and tools for
crowdsourcing disaster surveillance data; active disaster response system for
smart living environments; prototype components of disaster scenario record
capture and authoring system; and technical and theoretical results that
underpin these prototypes or enable us to bound the merits and limitations of
our solutions.